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Zhaocheng Zhu

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A*Net: A Scalable Path-based Reasoning Approach for Knowledge Graphs
Zhaocheng Zhu
Xinyu Yuan
Mikhail Galkin
Sophie Xhonneux
Ming Zhang
Maxime Gazeau
Jian Tang
Reasoning on large-scale knowledge graphs has been long dominated by embedding methods. While path-based methods possess the inductive capac… (voir plus)ity that embeddings lack, their scalability is limited by the exponential number of paths. Here we present A*Net, a scalable path-based method for knowledge graph reasoning. Inspired by the A* algorithm for shortest path problems, our A*Net learns a priority function to select important nodes and edges at each iteration, to reduce time and memory footprint for both training and inference. The ratio of selected nodes and edges can be specified to trade off between performance and efficiency. Experiments on both transductive and inductive knowledge graph reasoning benchmarks show that A*Net achieves competitive performance with existing state-of-the-art path-based methods, while merely visiting 10% nodes and 10% edges at each iteration. On a million-scale dataset ogbl-wikikg2, A*Net not only achieves a new state-of-the-art result, but also converges faster than embedding methods. A*Net is the first path-based method for knowledge graph reasoning at such scale.
2023-12-11
Neural Information Processing Systems (Accept (poster))
doi.org
openreview.net
Large Language Models can Learn Rules
Zhaocheng Zhu
Yuan Xue
Xinyun Chen
Denny Zhou
Jian Tang
Dale Schuurmans
Hanjun Dai
2023-10-10
ArXiv (prépublication)
doi.org
openreview.net
Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets (Ultra Large Dataset)
Dominique Beaini
Shenyang Huang
Joao Alex Cunha
Zhiyi Li
Gabriela Moisescu-Pareja
Oleksandr Dymov
Samuel Maddrell-Mander
Callum McLean
Ali Parviz
Luis T. Díaz Müller
Jama Hussein Mohamud
Frederik Wenkel
Michael Craig
Michał Koziarski
Jiarui Lu
Zhaocheng Zhu
Cristian Gabellini
Guillaume Rabusseau
Reihaneh Rabbany
Jian Tang … (voir 8 de plus)
Christopher G. Morris
Mirco Ravanelli
Guy Wolf
Prudencio Tossou
Hadrien Mary
Błażej Banaszewski
Chad Martin
Dominic Masters
2023-09-21
Zenodo (inconnu)
doi.org
Inductive Logical Query Answering in Knowledge Graphs
Mikhail Galkin
Zhaocheng Zhu
Hongyu Ren
Jian Tang
Formulating and answering logical queries is a standard communication interface for knowledge graphs (KGs). Alleviating the notorious incomp… (voir plus)leteness of real-world KGs, neural methods achieved impressive results in link prediction and complex query answering tasks by learning representations of entities, relations, and queries. Still, most existing query answering methods rely on transductive entity embeddings and cannot generalize to KGs containing new entities without retraining the entity embeddings. In this work, we study the inductive query answering task where inference is performed on a graph containing new entities with queries over both seen and unseen entities. To this end, we devise two mechanisms leveraging inductive node and relational structure representations powered by graph neural networks (GNNs). Experimentally, we show that inductive models are able to perform logical reasoning at inference time over unseen nodes generalizing to graphs up to 500% larger than training ones. Exploring the efficiency--effectiveness trade-off, we find the inductive relational structure representation method generally achieves higher performance, while the inductive node representation method is able to answer complex queries in the inference-only regime without any training on queries and scales to graphs of millions of nodes. Code is available at https://github.com/DeepGraphLearning/InductiveQE.
2022-11-28
Conference on Neural Information Processing Systems (Accept)
doi.org
openreview.net
Neural-Symbolic Models for Logical Queries on Knowledge Graphs
Zhaocheng Zhu
Mikhail Galkin
Zuobai Zhang
Jian Tang
Answering complex first-order logic (FOL) queries on knowledge graphs is a fundamental task for multi-hop reasoning. Traditional symbolic me… (voir plus)thods traverse a complete knowledge graph to extract the answers, which provides good interpretation for each step. Recent neural methods learn geometric embeddings for complex queries. These methods can generalize to incomplete knowledge graphs, but their reasoning process is hard to interpret. In this paper, we propose Graph Neural Network Query Executor (GNN-QE), a neural-symbolic model that enjoys the advantages of both worlds. GNN-QE decomposes a complex FOL query into relation projections and logical operations over fuzzy sets, which provides interpretability for intermediate variables. To reason about the missing links, GNN-QE adapts a graph neural network from knowledge graph completion to execute the relation projections, and models the logical operations with product fuzzy logic. Experiments on 3 datasets show that GNN-QE significantly improves over previous state-of-the-art models in answering FOL queries. Meanwhile, GNN-QE can predict the number of answers without explicit supervision, and provide visualizations for intermediate variables.
2022-06-27
Proceedings of the 39th International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding
Minghao Xu
Zuobai Zhang
Jiarui Lu
Zhaocheng Zhu
Yangtian Zhang
Chang Ma
Runcheng Liu
Jian Tang
We are now witnessing significant progress of deep learning methods in a variety of tasks (or datasets) of proteins. However, there is a lac… (voir plus)k of a standard benchmark to evaluate the performance of different methods, which hinders the progress of deep learning in this field. In this paper, we propose such a benchmark called PEER, a comprehensive and multi-task benchmark for Protein sEquence undERstanding. PEER provides a set of diverse protein understanding tasks including protein function prediction, protein localization prediction, protein structure prediction, protein-protein interaction prediction, and protein-ligand interaction prediction. We evaluate different types of sequence-based methods for each task including traditional feature engineering approaches, different sequence encoding methods as well as large-scale pre-trained protein language models. In addition, we also investigate the performance of these methods under the multi-task learning setting. Experimental results show that large-scale pre-trained protein language models achieve the best performance for most individual tasks, and jointly training multiple tasks further boosts the performance. The datasets and source codes of this benchmark are all available at https://github.com/DeepGraphLearning/PEER_Benchmark
2021-12-31
Advances in Neural Information Processing Systems 35 (NeurIPS 2022) (publié)
doi.org
openreview.net